Image blur may be caused by a number of reasons, such as improper focus on the subject, motion of the camera and/or motion of the subject during exposure. One method that currently exists for determining whether an image is blurred involves associating a degree of blur with edge spreading in the image, or conversely, with the sharpened edges in the image. Blurred edges seem to get wider as they lose their sharpness. These blurred edges, if observed on a small scale, become thinner and seem to recover their sharpness while the sharp edges will remain the same. Thus, the difference between a “sharp” and “blurry” edge may change due to scale and cause a blurry edge to appear more similar to a sharp edge. This similarity, in effect, results in difficulty associating a degree of blur to the images.
Conventional blur estimation techniques implement single-ended approaches for estimating image blur. Additionally, these techniques are typically universal and usually do not take into account specific features of different classes of subjects, in particular the face of a person. For example, some universal blur estimation techniques define blur on the basis of motion estimation or changes in size of detected subjects in a video. Such universal techniques do not take into account features specific to the type of subject (e.g., facial image) being analyzed and oftentimes produce unreliable results or require lengthy analysis.